code: def _transform_scaling(est, in_names=None):
      if in_names is None:
          in_names = _get_feature_names(
              est,
              feature_names=in_names,
              num_features=est.scale_.shape[0],
          )
      return [name for name in in_names]
  
file_path: eli5/eli5/sklearn/transform.py
imports: import numpy
  from sklearn.pipeline import Pipeline, FeatureUnion
  from sklearn.feature_selection.base import SelectorMixin
  from sklearn.preprocessing import MinMaxScaler, StandardScaler, MaxAbsScaler, RobustScaler
  from eli5.transform import transform_feature_names
  from eli5.sklearn.utils import get_feature_names
label_desc: Use image node_id 2 for calling a global function with example usage: _transform_scaling(est, in_names) and returns: unknown